Electroencephalogram (EEG) is a non-invasive, high temporal-resolution technique for monitoring brain activity. However, affected by the volume conduction effect, EEG has a low spatial resolution and is difficult to locate brain neuronal activity precisely. The surface Laplacian (SL) technique obtains the Laplacian EEG (LEEG) by estimating the second-order spatial derivative of the scalp potential. LEEG can reflect the radial current activity under the scalp, with positive values indicating current flow from the brain to the scalp (“source”) and negative values indicating current flow from the scalp to the brain (“sink”). It attenuates signals from volume conduction, effectively improving the spatial resolution of EEG, and is expected to contribute to breakthroughs in neural engineering. This paper provides a systematic overview of the principles and development of SL technology. Currently, there are two implementation paths for SL technology: current source density algorithms (CSD) and concentric ring electrodes (CRE). CSD performs the Laplace transform of the EEG signals acquired by conventional disc electrodes to indirectly estimate the LEEG. It can be mainly classified into local methods, global methods, and realistic Laplacian methods. The global method is the most commonly used approach in CSD, which can achieve more accurate estimation compared with the local method, and it does not require additional imaging equipment compared with the realistic Laplacian method. CRE employs new concentric ring electrodes instead of the traditional disc electrodes, and measures the LEEG directly by differential acquisition of the multi-ring signals. Depending on the structure, it can be divided into bipolar CRE, quasi-bipolar CRE, tripolar CRE, and multi-pole CRE. The tripolar CRE is widely used due to its optimal detection performance. While ensuring the quality of signal acquisition, the complexity of its preamplifier is relatively acceptable. Here, this paper introduces the study of the SL technique in resting rhythms, visual-related potentials, movement-related potentials, and sensorimotor rhythms. These studies demonstrate that SL technology can improve signal quality and enhance signal characteristics, confirming its potential applications in neuroscientific research, disease diagnosis, visual pathway detection, and brain-computer interfaces. CSD is frequently utilized in applications such as neuroscientific research and disease detection, where high-precision estimation of LEEG is required. And CRE tends to be used in brain-computer interfaces, that have stringent requirements for real-time data processing. Finally, this paper summarizes the strengths and weaknesses of SL technology and envisages its future development. SL technology boasts advantages such as reference independence, high spatial resolution, high temporal resolution, enhanced source connectivity analysis, and noise suppression. However, it also has shortcomings that can be further improved. Theoretically, simulation experiments should be conducted to investigate the theoretical characteristics of SL technology. For CSD methods, the algorithm needs to be optimized to improve the precision of LEEG estimation, reduce dependence on the number of channels, and decrease computational complexity and time consumption. For CRE methods, the electrodes need to be designed with appropriate structures and sizes, and the low-noise, high common-mode rejection ratio preamplifier should be developed. We hope that this paper can promote the in-depth research and wide application of SL technology.

Objective To explore the relationship between PANoptosis and hepatic ischemia-reperfusion injury (HIRI), and to screen the key genes of PANoptosis in HIRI. Methods PANoptosis-related differentially expressed genes (PDG) were obtained through the Gene Expression Omnibus database and GeneCards database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the biological pathways related to PDG. A protein-protein interaction network was constructed. Key genes were selected, and their diagnostic value was assessed and validated in the HIRI mice. Immune cell infiltration analysis was performed based on the cell-type identification by estimating relative subsets of RNA transcripts. Results A total of 16 PDG were identified. GO analysis showed that PDG were closely related to cellular metabolism. KEGG analysis indicated that PDG were mainly enriched in cellular death pathways such as apoptosis and immune-related signaling pathways such as the tumor necrosis factor signaling pathway. GSEA results showed that key genes were mainly enriched in immune-related signaling pathways such as the mitogen-activated protein kinase (MAPK) signaling pathway. Two key genes, DFFB and TNFSF10, were identified with high accuracy in diagnosing HIRI, with areas under the curve of 0.964 and 1.000, respectively. Immune infiltration analysis showed that the control group had more infiltration of resting natural killer cells, M2 macrophages, etc., while the HIRI group had more infiltration of M0 macrophages, neutrophils, and naive B cells. Real-time quantitative polymerase chain reaction results showed that compared with the Sham group, the relative expression of DFFB messenger RNA in liver tissue of HIRI group mice increased, and the relative expression of TNFSF10 messenger RNA decreased. Cibersort analysis showed that the infiltration abundance of naive B cells was positively correlated with DFFB expression (r=0.70, P=0.035), and the infiltration abundance of M2 macrophages was positively correlated with TNFSF10 expression (r=0.68, P=0.045). Conclusions PANoptosis-related genes DFFB and TNFSF10 may be potential biomarkers and therapeutic targets for HIRI.

The quality of traditional Chinese medicine(TCM) is a critical foundation for ensuring the stability of its efficacy, as well as the safety and effectiveness of its clinical use. The identification of critical quality attributes(CQAs) is one of the core components of TCM preparation quality control. This study focuses on Jianwei Xiaoshi Tablets and explores their CQAs related to property and flavor from the perspective of taste receptor proteins. Three taste receptor proteins, T1R2, T1R3, and TRPV1, were selected, and a biosensor based on high-electron-mobility transistor(HEMT) was constructed to detect the interactions between Jianwei Xiaoshi Tablets and taste receptor proteins. Simultaneously, liquid chromatography-mass spectrometry(LC-MS) technology was used to analyze the chemical composition of Jianwei Xiaoshi Tablets. In examining the interaction strength, the results indicated that the interaction between Jianwei Xiaoshi Tablets and TRPV1 protein was the strongest, followed by T1R3, with the interaction with T1R2 being relatively weaker. By combining biosensing technology with LC-MS, 16 chemical components were identified from Jianwei Xiaoshi Tablets, among which six were selected as CQAs for sweetness and seven for pungency. Further validation experiments demonstrated that CQAs such as hesperidin and hesperetin had strong interactions with their corresponding taste receptor proteins. Through the combined use of multiple technological approaches, this study successfully determined the property and flavor-related CQAs of Jianwei Xiaoshi Tablets. It provides novel ideas and approach for the identification of CQAs in TCM preparations and offers comprehensive theoretical support for TCM quality control, contributing to the improvement and development of TCM preparation quality control systems.
Drugs, Chinese Herbal/chemistry* ; Biosensing Techniques/methods* ; TRPV Cation Channels/chemistry* ; Tablets/chemistry* ; Receptors, G-Protein-Coupled/genetics* ; Quality Control ; Taste ; Humans ; Mass Spectrometry

ObjectiveTo investigate the features of liver histopathological damage in patients with indeterminate phase of chronic HBV infection， as well as the timing for initiating antiviral therapy in such patients. MethodsA retrospective screening was performed for the patients with chronic HBV infection who were hospitalized in The Fifth Medical Center of Chinese PLA General Hospital and underwent liver biopsy from March 2018 to April 2022， among whom the patients who met the criteria for indeterminate phase defined in Chinese guidelines for chronic hepatitis B prevention and treatment （2022 edition） were enrolled， and their clinical data were collected. Liver histopathological stage was determined using the Scheuer scoring system， with stages 0 — 4 for inflammation grade （G） and stages 0 — 4 for fibrosis degree （S）， and the patients were divided into groups based on the presence of significant necroinflammation （≥G2） and significant liver fibrosis （≥S2）. The independent samples t-test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. A Spearman’s rank correlation analysis was used to investigate the correlation between liver histopathology and clinical factors， and the Logistic regression model was used to identify the independent influencing factors for significant necroinflammation and liver fibrosis. ResultsA total of 271 patients with indeterminate phase of chronic HBV infection were enrolled， among whom 61 （22.5%） had significant necroinflammation （≥G2） and 124 （45.8%） had significant liver fibrosis （≥S2）. The Logistic regression analysis showed that alanine aminotransferase ≥30 U/L （for male patients） or ≥19 U/L （for female patients） （odds ratio ［OR］=2.69， 95% confidence interval ［CI］： 1.39 — 5.21， P=0.003）， HBV DNA ≥2 000 IU/mL （OR=2.75， 95%CI： 1.38 — 5.48， P=0.004）， and liver stiffness measurement （LSM） ≥6.0 kPa （OR=4.57， 95%CI： 2.17 — 9.62， P<0.001） were independent risk factors for significant inflammation. HBV DNA ≥2 000 IU/mL （OR=1.82， 95%CI： 1.01 — 3.32， P=0.049） and LSM ≥6.0 kPa （OR=2.06， 95%CI： 1.23 — 3.43， P=0.006） were independent influencing factors for significant liver fibrosis. ConclusionAmong the patients with indeterminate phase of chronic HBV infection， a substantial proportion of patients have significant liver histopathological damage. Antiviral therapy should be initiated in a timely manner for patients with high-risk factors.

ObjectiveTo investigate the incidence rate of primary liver cancer （PLC） and the progression of liver fibrosis in chronic hepatitis B （CHB） patients with low-level viremia （LLV） （HBV DNA<2 000 IU/mL but ≥20 IU/mL） after treatment adjustment， and to provide more robust evidence for clinical practice. MethodsA retrospective analysis was performed for the clinical data of LLV patients who initially received nucleos（t）ide analogue （NAs） for at least 48 weeks at the Fifth Medical Center of PLA General Hospital from August 2007 to April 2017 and subsequently underwent NAs adjustment due to LLV， and according to the virologic response after 48 weeks of treatment adjustment， the patients were divided into LLV group and complete virological response （CVR） group （HBV DNA<20 IU/mL）. The patients were followed up once every 3‍ ‍—‍ ‍6 months till the primary endpoint event of PLC or October 2024. The incidence rate of PLC and the progression of liver fibrosis were observed， and the progression of liver fibrosis was defined as an increase of ≥1 grade in fibrosis-4 （FIB-4） index. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of continuous data with skewed distribution between two groups； the chi-square test was used for comparison of categorical data between groups. The Kaplan-Meier method was used to calculate the cumulative incidence rate of PLC， and the Log-rank test was used for comparison between groups； the Cox regression analysis was used to investigate the risk factors for PLC， and the Logistic regression analysis was used to investigate the influencing factors for the progression of liver fibrosis. ResultsA total of 307 patients were enrolled， with a mean age of 50.0 years， and the male patients accounted for 80.5%. After 48 weeks of treatment with the adjusted NAs regimen， 254 patients （82.7%） achieved CVR， and 53 patients （17.3%） still had LLV. For the LLV group， the incidence rate of PLC was 30.2% and the rate of liver fibrosis progression was 22.6%， while for the CVR group， the incidence rate of PLC was only 13.4%， and the rate of liver fibrosis progression was 7.5%. The multivariate regression analyses showed that LLV was an independent risk factor for the onset of PLC （hazard ratio=2.623， 95% confidence interval ［CI］： 1.315‍ ‍—‍ ‍5.234， P=0.006） and the progression of liver fibrosis （odds ratio=3.213， 95%CI： 1.385‍ ‍—‍ ‍7.455， P=0.007）. ConclusionActive adjustment of treatment is needed immediately after the diagnosis of LLV to improve CVR， and if LLV persists after treatment adjustment， it is necessary to enhance the monitoring of liver fibrosis progression and PLC， so as to facilitate early diagnosis and treatment.

This paper focuses on the application of health big data in cancer screening. Firstly, the sources and characteristics of health big data are introduced, then the commonly used epidemiological designs and analytical techniques in hospital-based cancer screening studies are summarized and the application scenarios of such studies are described. Finally, the challenges and future development in the application of health big data are analyzed to provide reference for the future studies.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

BACKGROUND: Non-invasive computed tomography angiography (CTA)-based fractional flow reserve (CT-FFR) could become a gatekeeper to invasive coronary angiography. Deep learning (DL)-based CT-FFR has shown promise when compared to invasive FFR. To evaluate the performance of a DL-based CT-FFR technique, DeepVessel FFR (DVFFR). METHODS: This retrospective study was designed for iScheMia Assessment based on a Retrospective, single-center Trial of CT-FFR (SMART). Patients suspected of stable coronary artery disease (CAD) and undergoing both CTA and invasive FFR examinations were consecutively selected from the Beijing Anzhen Hospital between January 1, 2016 to December 30, 2018. FFR obtained during invasive coronary angiography was used as the reference standard. DVFFR was calculated blindly using a DL-based CT-FFR approach that utilized the complete tree structure of the coronary arteries. RESULTS: Three hundred and thirty nine patients (60.5 ±10.0 years and 209 men) and 414 vessels with direct invasive FFR were included in the analysis. At per-vessel level, sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of DVFFR were 94.7%, 88.6%, 90.8%, 82.7%, and 96.7%, respectively. The area under the receiver operating characteristics curve (AUC) was 0.95 for DVFFR and 0.56 for CTA-based assessment with a significant difference (P < 0.0001). At patient level, sensitivity, specificity, accuracy, PPV and NPV of DVFFR were 93.8%, 88.0%, 90.3%, 83.0%, and 95.8%, respectively. The computation for DVFFR was fast with the average time of 22.5 ± 1.9 s. CONCLUSIONS The results demonstrate that DVFFR was able to evaluate lesion hemodynamic significance accurately and effectively with improved diagnostic performance over CTA alone. Coronary artery disease (CAD) is a critical disease in which coronary artery luminal narrowing may result in myocardial ischemia. Early and effective assessment of myocardial ischemia is essential for optimal treatment planning so as to improve the quality of life and reduce medical costs.

Cardiac arrest (CA) is a critical condition in the field of cardiovascular medicine. Despite successful resuscitation, patients continue to have a high mortality rate, largely due to post CA syndrome (PCAS). However, the injury and pathophysiological mechanisms underlying PCAS remain unclear. Experimental animal models are valuable tools for exploring the etiology, pathogenesis, and potential interventions for CA and PCAS. Current CA animal models include electrical induction of ventricular fibrillation (VF), myocardial infarction, high potassium, asphyxia, and hemorrhagic shock. Although these models do not fully replicate the complexity of clinical CA, the mechanistic insights they provide remain highly relevant, including post-CA brain injury (PCABI), post-CA myocardial dysfunction (PAMD), systemic ischaemia/reperfusion injury (IRI), and the persistent precipitating pathology. Summarizing the methods of establishing CA models, the challenges encountered in the modeling process, and the mechanisms of PCAS can provide a foundation for developing standardized CA modeling protocols.
Animals ; Disease Models, Animal ; Post-Cardiac Arrest Syndrome/physiopathology* ; Heart Arrest/physiopathology* ; Humans ; Ventricular Fibrillation/complications*